Introduction to Quantitative Political Research
PSCI 3300.003 Political Science Research Methods
A. Jordan Nafa
University of North Texas
September 1st, 2022
How Should we Study Politics?
Political science is a field characterized by a diverse range of approaches to inquiry and debates about how we ought to study political phenomena have long animated the discipline.
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Normative
- Focused on subjective, moral questions about the world and how things ought to be.
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Empirical
- Focused on objective explanation and description, questions about how the world is and why.
\[ \definecolor{treatment}{RGB}{255, 53, 94} \definecolor{treat}{RGB}{253, 91, 120} \definecolor{orange}{RGB}{255, 96, 55} \definecolor{neonorange}{RGB}{255, 153, 51} \definecolor{lime}{RGB}{204, 255, 0} \definecolor{resp}{RGB}{102, 255, 102} \definecolor{index}{RGB}{170, 240, 209} \definecolor{untreat}{RGB}{80, 191, 230} \definecolor{pink}{RGB}{255, 110, 255} \definecolor{sample}{RGB}{255, 0, 204} \definecolor{operator}{RGB}{255,255,255} \]
Normative Approaches
Normative approaches to the study of politics date back thousands of years and feature prominently in the sub-field of political philosophy.
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How should the world look? Asks for a moral judgement
Who should be responsible for paying for the consequences of climate change?
Should we fire Elon Musk into the Sun?
Should women have autonomy over their reproductive choices?
Is it fair to forgive student loan debt?
Normative arguments are common in certain areas of law and philosophy but have no place in this course as they do not lend themselves to scientific answers
Empirical Approaches
Empirical approaches are those that aim to apply the scientific method to the study of politics and hold a dominant place contemporary political science.
Empirical approaches can be descriptive or causal, quantitative or qualitative, experimental or observational but they all aim to answer some question about how, what, or why the world is.
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Description focuses on observing and measuring the state of the world; it aims to answer questions about who or what in relation to some phenomena (Gerring 2012).
What is democracy and how can we operationalize it?
Who won the 2020 presidential election election?
2020 Presidential Election Vote Totals
Evolution of Liberal Democracy in America
Empirical Approaches
Causal approaches are concerned with explaining why some phenomenon occurs in the world (Samii 2016).
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Contemporary political science is a discipline interested in answering causal questions.
Why do poor conservatives tend to vote against their own economic interests?
How do gender-inclusive peace processes influence the risk of conflict recurrence?
How would the world change if we fired Elon Musk into the Sun?
Our focus in this class will be primarily on causal questions and entirely on empirical approaches to the study of politics
What is Causal Inference?
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Does forgiving student loan debt increase inflation?
Imagine student loan debt is forgiven and inflation increases
Would this increase have happened if student loan debt had not been forgiven?
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How do gender-inclusive peace processes influence the risk of conflict recurrence?
Conflicts that terminate with gender-inclusive peace provisions tend to be less likely to recurr
Would conflict have recurred in the abscence of these gender-inclusive peace provisions?
Causal inference is about counterfactuals
What is Causal Inference?
A counterfactual is what would have happened in the absence of some intervention. Counterfactuals are questions about the data we do not observe, not the data we do.
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Imagine a study of \(\color{sample} n\) individuals
\(\color{treat} n_{1}\) are assigned some treatment
\(\color{untreat} n_{0}\) do not receive the treatment
For each individual \(\color{index} i \color{operator}\in \{1, 2, \dots, \color{sample} n \color{operator}\}\) we observe the outcome \(\color{resp}Y_{\color{index}i}\)
Treatment status for each individual \(\color{index} i\) \[\color{treatment} X_{\color{index}i} \color{operator} = \begin{cases}\color{treat} 1 \text{ if treated}\\ \color{untreat} 0 \text{ if not treated}\end{cases}\]
What is Causal Inference?
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We want to know the causal effect of \(X_{i}\) on \(Y_{i}\)
If a respondent is treated, \(X_{i} = 1\) and we observe some value of \(Y_{i}\)
What value of \(Y_{i}\) would we have observed if \(X_{i} = 0\) instead?
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Fundamental Problem of Causal Inference
For each individual \(i\) we can only observe \(X_{i} = 1\) or \(X_{i} = 0\)
Causal inference is a missing data problem
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How do we overcome this problem?
We make assumptions to bridge these parallel worlds
We will spend the rest of the semester on this
Why Causal Inference?
As it turns out, causal inference is really, really hard so why bother at all?
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We could just make some claims and use a bunch of weasel words to avoid saying “cause” and “effect” while still heavily implying causality, right?
“\(X\) explains \(Y\)”
“\(X\) has an impact on \(Y\)”
“People who do \(X\) are more likely to experience \(Y\)”
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Lots of people still do this!
Makes it hard to distinguish between what is real and what is not, results in the proliferation of pseudo-facts (Samii 2016)
Need to be explicit about our assumptions, intentions, and goals
What Causal Inference is Not
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Descriptions of how the world is, correlations, joint distributions, predictions, regression coefficients, odds ratios, probabilities, etc.
All of these things may be useful and some may have causal interpretations under specific circumstances
They do not, however, in and of themselves capture causal relationships without additional assumptions
A causal effect is the change we would observe if we manipulated some feature of the world while holding all else constant
References
